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    Competition on Spatial Statistics for Large Datasets

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    mainfinalGenton.pdf
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    1.518Mb
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    Description:
    Accepted manuscript
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    Type
    Article
    Authors
    Huang, Huang cc
    Abdulah, Sameh
    Sun, Ying cc
    Ltaief, Hatem cc
    Keyes, David E. cc
    Genton, Marc G. cc
    KAUST Department
    Applied Mathematics and Computational Science Program
    Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
    Environmental Statistics Group
    Extreme Computing Research Center
    Office of the President
    Spatio-Temporal Statistics and Data Analysis Group
    Statistics Program
    Date
    2021-07-08
    Online Publication Date
    2021-07-08
    Print Publication Date
    2021-12
    Embargo End Date
    2022-07-08
    Submitted Date
    2021-04-20
    Permanent link to this record
    http://hdl.handle.net/10754/670104
    
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    Abstract
    As spatial datasets are becoming increasingly large and unwieldy, exact inference on spatial models becomes computationally prohibitive. Various approximation methods have been proposed to reduce the computational burden. Although comprehensive reviews on these approximation methods exist, comparisons of their performances are limited to small and medium sizes of datasets for a few selected methods. To achieve a comprehensive comparison comprising as many methods as possible, we organized the Competition on Spatial Statistics for Large Datasets. This competition had the following novel features: (1) we generated synthetic datasets with the ExaGeoStat software so that the number of generated realizations ranged from 100 thousand to 1 million; (2) we systematically designed the data-generating models to represent spatial processes with a wide range of statistical properties for both Gaussian and non-Gaussian cases; (3) the competition tasks included both estimation and prediction, and the results were assessed by multiple criteria; and (4) we have made all the datasets and competition results publicly available to serve as a benchmark for other approximation methods. In this paper, we disclose all the competition details and results along with some analysis of the competition outcomes.
    Citation
    Huang, H., Abdulah, S., Sun, Y., Ltaief, H., Keyes, D. E., & Genton, M. G. (2021). Competition on Spatial Statistics for Large Datasets. Journal of Agricultural, Biological and Environmental Statistics. doi:10.1007/s13253-021-00457-z
    Sponsors
    Funding was provided by King Abdullah University of Science and Technology.
    Publisher
    Springer Science and Business Media LLC
    Journal
    Journal of Agricultural, Biological and Environmental Statistics
    DOI
    10.1007/s13253-021-00457-z
    Additional Links
    https://link.springer.com/10.1007/s13253-021-00457-z
    ae974a485f413a2113503eed53cd6c53
    10.1007/s13253-021-00457-z
    Scopus Count
    Collections
    Articles; Applied Mathematics and Computational Science Program; Extreme Computing Research Center; Statistics Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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